Evaluation of ADAS with a supported-driver model for desired allocation of tasks between human and technology performance

Partly automated driving is relevant for solving mobility problems, but also causes concerns with respect to the driver‟s reliability in task performance. The supported driver model presented in this paper is therefore intended to answer the question, what type of support and in which circumstances, will enhance the driver‟s ability to control the vehicle. It became apparent that prerequisites for performing tasks differ per driving task‟s type and require different support. The possible support for each driving task‟s type, has been combined with support-types to reduce the error causations from each different performance level (i.e. knowledge-based, rule-based and skill-based performance). The allocation of support in relation to performance level and driving task‟s type resulted in a supported driver model and this model relates the requested circumstances to appropriate support types. Among three tested ADAS systems, semiautomated parking showed best allocation of support; converting the demanding parallel parking task into a rather routine-like operation.